This information is for reference purposes only. It was current when produced and may now be outdated. Archive material is no longer maintained, and some links may not work. Persons with disabilities having difficulty accessing this information should contact us at: https://info.ahrq.gov. Let us know the nature of the problem, the Web address of what you want, and your contact information.

State programs often do not know how to use data from health plans and providers to
effectively design, implement, monitor, and evaluate their purchasing efforts. This session
examined the efforts of purchasers that sought to make better use of the data they collected to
inform their health care purchasing activities.

Many States have improved the amount and quality of data they collect. Yet Michael Bailit
observed that some States have been unable to ensure that these data are being used to support
quality improvement initiatives. Several steps are necessary to maximize the
usability of data:

Define data across a broad array of program performance priorities (and the collection
strategy should support the higher of these priorities).

Encounter data are used to analyze differences in plan enrollment and use, and in
case mix across categories and plans.

MassHealth has also used data analysis results to develop financial incentives and disincentives
in its contracting with plans—incentives are attached to goals to make significant improvement
in performance, while disincentives are attached to failure to improve.

Wisconsin Medicaid

Angela Dombrowicki from the Wisconsin Medicaid program offered another
example of successfully using data, describing the State's use of encounter data as part of a
multi-pronged, evolving quality improvement strategy. Achieving uniform encounter data—accurate records of service provided to all Medicaid recipients enrolled in the managed care
system—is beneficial because it:

Reduces administrative effort by the State and HMOs by using only one data set for
quality improvement activities.

Permits data analysis for any chosen indicator/process/outcome for which there are
data.

Wisconsin collected and published survey data prior to gathering encounter data, which were
collected annually from HMOs by asking them specific questions about a segment of their
population. These data tended to be incomplete, difficult to validate without extensive chart
audits, difficult to use for trending, untimely, and inaccurate. Encounter data are submitted
monthly and are complete, easily verifiable, flexible, and timely.

Some of the reasons the encounter data system has been successful in Wisconsin are:

The State made the new data system a priority and worked intensively with HMOs over
18 months to get feedback on a sample data set, and designed this system around what
HMOs could handle.

The contract language was strengthened to include strict compliance penalties for data
submission.

Other reporting requirements were relaxed somewhat to focus primarily on developing
this system.

The system continued building upon past successes of data validity audits by reviewing
two areas on-site with HMOs: the capability of the HMO data system, and sample chart
reviews of selected data.

Wisconsin's experience can inform others about how to use data for quality improvement. In
Medicaid managed care, a data system like Wisconsin's can assist HMOs improving
management of certain conditions, such as childhood asthma. State Medicaid programs and
other payers may also learn from Wisconsin's current initiative, the Minimal Operational Data
Set (MODS). MODS aims to provide a master database/Web site for consumers, policymakers,
researchers, legislators, and the general public that will inform them about HMO performance
in common quality areas.